HSN/376 Week 3: Clinical Decision Support Tools, sample paper

A Risk Score at Admission and an Alert Nobody Reads: Clinical Decision Support for Postpartum Hemorrhage Risk on a Labor Unit

[Student Name]

University of Phoenix

HSN/376: Health Information Technology for Nursing

Week 3 Assignment

[Instructor Name]

[Date]

The birthing center, its alert and its data are a composite written for a model paper.

Clinical decision support is the part of the electronic record designed to help clinicians decide, not only to remember. It includes alerts, reminders, order sets, risk calculators and links to guidance delivered at the moment of care. Sutton et al. (2020) describe its benefits, including safer medication use, better adherence to guidelines and more consistent documentation, and its risks, including alert fatigue, workflow disruption and dependence on data that may be wrong. This paper examines one decision support tool on my birthing center: the postpartum hemorrhage risk assessment built into the admission workflow.

The Decision the Tool Supports

Postpartum hemorrhage is a leading cause of severe maternal illness and death, and many hemorrhages occur in patients who were not identified as high risk. Risk assessment at admission is meant to prepare the team: a patient at medium risk has a blood type and screen drawn, and a patient at high risk has blood crossmatched and ready, with the team briefed before birth.

The evidence for the tool comes from risk stratification research. Dilla et al. (2013) applied the California Maternal Quality Care Collaborative risk criteria to 10,134 women who gave birth at one hospital over a year and found that significant hemorrhage, defined as bleeding that required transfusion of at least one unit of red blood cells, occurred in 0.8% of low-risk, 2.0% of medium-risk and 7.3% of high-risk women. The categories separated risk clearly, although hemorrhage still occurred in the low-risk group, which is why every patient needs preparation, not only those flagged.

How the Tool Works on My Unit

At admission, the nurse completes a structured risk assessment with about fifteen yes-or-no items, such as prior cesarean birth, multiple gestation, more than four prior births, chorioamnionitis, known placenta previa and a low platelet count. The record assigns a risk level and, for medium and high risk, displays an alert that recommends the matching laboratory orders and asks the nurse to notify the provider. The assessment is supposed to be repeated when risk changes, for example when labor is prolonged or magnesium sulfate is started.

What Went Wrong

Within six months of go-live, the unit had a problem. The alert fired for most admissions, because common items such as a prior cesarean birth or an induction placed many patients in the medium group. Nurses reported that the alert appeared on the same screen as three other admission alerts and that they closed it to finish admission. A review of 100 admissions showed that the alert was acknowledged in all cases but that the recommended type and screen was ordered on time in only 61 of the 71 medium-risk and high-risk patients. Reassessment during labor, which the tool could not trigger by itself, was documented in fewer than half of eligible cases.

This pattern is common. A review of seventeen studies of computerized drug safety alerts found that clinicians overrode between 49% and 96% of them (van der Sijs et al., 2006). The authors argued that overriding often reflects the alert's own design, such as low specificity, unclear content and interruptions that come at the wrong point in the work, rather than carelessness. An alert that fires for most patients stops carrying information, because it no longer tells the nurse anything about this patient.

Redesign Proposals

Based on the review and the published evidence, the practice council proposed changes.

Fewer interruptive alerts. Medium risk would no longer produce a pop-up; instead, the laboratory orders would be preselected in the admission order set for the provider to sign. Only high risk would interrupt, and the alert would be sent to both the nurse and the provider.

Placement in the workflow. The risk level would appear in the patient banner at the top of every screen, so it is visible without an alert.

Automatic reassessment. The record would prompt reassessment when a new risk factor is documented elsewhere, such as the start of magnesium sulfate or labor lasting beyond a set number of hours, rather than depending on memory.

Monitoring. The unit would review alert override rates and on-time type and screen orders quarterly, with a target of at least 95% of high-risk patients having blood crossmatched before birth.

Balancing Benefit and Burden

Removing the medium-risk pop-up raises a fair objection: an alert that some nurses ignore still reaches the nurses who read it, and taking it away might lose those patients. The council answered the objection with data rather than opinion. In the 100 reviewed admissions, the pop-up had not raised on-time type and screen orders above what the preselected order set would produce, because providers, not nurses, place the orders, and the alert went only to the nurse. Moving the recommendation into the order set puts it in front of the person who acts on it. The council also agreed to watch for harm from the change: if on-time type and screen orders for medium-risk patients fell below their current level in the first quarter after the redesign, the pop-up would return while the design was reconsidered. Setting that rule in advance keeps the redesign honest, because a change meant to reduce alert fatigue must not quietly reduce safety.

The Nurse's Role in Decision Support

Decision support tools are often designed by informatics teams and vendors, but nurses are their main users on a labor unit. The redesign above came from nurses who noticed that the alert was being dismissed and asked why. Sutton et al. (2020) note that decision support succeeds when it is designed with end users and evaluated after implementation, which requires clinicians willing to report when a tool is failing. Staff nurses are well placed to do that because they see the tool in use every shift.

Conclusion

A postpartum hemorrhage risk tool rests on good evidence that risk levels predict bleeding. On my unit, its design turned that evidence into an alert that nurses closed without reading. Fewer and better-placed alerts, automatic reassessment and regular monitoring could restore its value. Week 4 turns from one tool to a larger question: how an organization evaluates an information system after it goes live.

References

Dilla, A. J., Waters, J. H., & Yazer, M. H. (2013). Clinical validation of risk stratification criteria for peripartum hemorrhage. Obstetrics & Gynecology, 122(1), 120-126. https://doi.org/10.1097/AOG.0b013e3182941c78

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. npj Digital Medicine, 3, Article 17. https://doi.org/10.1038/s41746-020-0221-y

van der Sijs, H., Aarts, J., Vulto, A., & Berg, M. (2006). Overriding of drug safety alerts in computerized physician order entry. Journal of the American Medical Informatics Association, 13(2), 138-147. https://doi.org/10.1197/jamia.M1809